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A Bayesian Network-Based Software Requirement Complexity Prediction Model

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Computational Methods and Data Engineering

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 139))

Abstract

Software development has an inflated probability of project failure and the major reason for it is the poor requirement engineering process. Potential threats or risks related to requirements must be identified at the earlier stages of the development itself, so as to minimize the negative impact of subsequent affects. Researches reveal that VUCA risks, i.e., Requirement Volatility, Requirement Uncertainty, Requirement Complexity, and Requirement Ambiguity, are the basic sources of risks for other risks too. Complexity in requirements is one of the important factors affecting quality of the product. Computing and analysis of the product complexity in the requirement analysis phase of SDLC will give benefits in assessing the required development and testing efforts for the prospective software product. Failing to which, software designers and testers will need further clarification, thus slowing down the design and verification process. This paper attempts to establish a connection between the VUCA risks and propose a methodology to minimize requirement complexity. The various factors affecting requirement complexity are identified, in the requirement engineering phase. A Bayesian approach is proposed to predict the requirement complexity. The proposed model uses various complexity factors found through extensive literature review to manage requirement complexity of the software products.

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Author information

Authors and Affiliations

  1. Department of Computer Science &, Engineering Integral University, Lucknow, India

    Halima Sadia

  2. Ambalika Institute of Technology & Management, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, India

    Syed Qamar Abbas

  3. Department of Computer Application, Integral University, Lucknow, India

    Mohammad Faisal

Authors
  1. Halima Sadia
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  2. Syed Qamar Abbas
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  3. Mohammad Faisal
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Corresponding author

Correspondence to Halima Sadia .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, University of Dayton, Dayton, OH, USA

    Vijayan K. Asari

  2. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Vijendra Singh

  3. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Rajkumar Rajasekaran

  4. Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India

    R. B. Patel

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Sadia, H., Abbas, S.Q., Faisal, M. (2023). A Bayesian Network-Based Software Requirement Complexity Prediction Model. In: Asari, V.K., Singh, V., Rajasekaran, R., Patel, R.B. (eds) Computational Methods and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 139. Springer, Singapore. https://doi.org/10.1007/978-981-19-3015-7_15

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